What is it about?
This is guide to create metrics for measuring quality of dynamic point clouds (3D videos). This work aims to provide a direction on how to apply a temporal pooling function to combine per-frame quality predictions generated with descriptor-based PC quality assessment methods to estimate the quality of dynamic PCs. We have shown for the first time that the performance of temporal pooling is consistently better when a temporal variation pooling is used.
Featured Image
Photo by stem.T4L on Unsplash
Why is it important?
The study provides insights into designing immersive 3D video quality metrics. This task is fundamental to allow a variety of applications that enable the consumption of these videos.
Perspectives
This study aims to help professionals in the field of multimedia, image processing and computer vision, especially those who work with immersive 3D videos based on point clouds.
Pedro Freitas
Samsung Electronics
Read the Original
This page is a summary of: Comparative Evaluation of Temporal Pooling Methods for No-Reference Quality Assessment of Dynamic Point Clouds, October 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3552482.3556552.
You can read the full text:
Resources
Repository: No-Reference DPCQA Temporal Pooling
Implementation of temporal pooling methods investigated in the paper "Comparative Evaluation Of Temporal Pooling Methods For No-Reference Dynamic Point Cloud Quality Assessment Quality Assessment".
Repository: DPC Temporal Pooling
Implementation of temporal pooling methods investigated in the paper "On the Performance of Temporal Pooling Methods for Quality Assessment of Dynamic Point Clouds".
Paper: On the Performance of Temporal Pooling Methods for Quality Assessment of Dynamic Point Clouds
Point Clouds (PCs) are collections of points distributed in the 3D space, containing attributes such as color, normals, transparency, and specularity. Dynamic Point Clouds (DPCs) correspond to sequences of points in the 3D space that vary over time like pixels vary over time in a conventional video. Dynamic PCs are a suitable way to represent volumetric videos that can be used in augmented or virtual reality applications. This representation, however, requires a large number of points to achieve a high quality of experience and needs to be compressed before storage and transmission. Therefore, reliable quality metrics are needed in order to automatically estimate the perceptual quality of dynamic PC contents. Since currently there are several quality assessment metrics for static PC, a possible approach solution consists of using temporal pooling functions to combine the quality scores predicted for each of the frames. In this paper, we study the effects of different temporal pooling strategies on the performance of dynamic PC quality assessment methods. Our experimental tests were performed using a recent publicly-available database, demonstrating the efficiency of the evaluated temporal pooling models. More specifically, the work provides a recipe on how to apply a temporal pooling function to combine frame-based quality predictions generated with texture-based static PC quality assessment methods to estimate the quality of dynamic PCs.
Contributors
The following have contributed to this page
